5 Ways Data Science and Analytics Has Changed the Financial Services Industry

5 ways data science and analytics have changed the financial services industry

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Data science and analytics is a crucial part of the decision making process for all small and large scale businesses wishing to remain competitive. This includes banks and companies in the financial services industry, which are now using big data analytics and data management tools to obtain and leverage insights that better inform decisions around risk management, profitability, performance, and organisational success.

The rise of big data in the financial services industry began to surge just after the great financial recession hit in 2008. Consumers could no longer trust banks, investments saw a downfall, unemployment peaked, and strict policies and regulations were put into place. At the same time, banks started to digitise their service offerings and as more financial transactions occurred online, risks in cyber security, data fraud, and hacking climbed. Subsequently, big data analytics and data management systems were, and continue to be, employed to ensure the security of large volume data flows.

As a result of this, the demand for finance professionals that can successfully marry their domain knowledge with practical data science and analytics skills has grown exponentially. Companies are seeking to hire data professionals from a finance background in order to adapt to evolving business needs and cope with an industry that is ruled by big data, advanced analytics, machine learning and AI.

Let’s dig a little deeper into the ways in which data science and analytics has transformed the financial services industry.

Data science and analytics is changing the way financial institutions monitor market activity

tech professional analysing data science and analytics in financial institution

Financial fraud is one of the biggest challenges faced by financial institutions and ensuring that customer data, investments, and transactions are protected and secure is obligatory to function. The demand for data literacy in the finance sector is particularly pronounced when you consider the unprecedented amount of credit card fraud today, triggered by growing e-commerce activity.

However, professionals in the industry aren’t the only ones with big data skills – criminals are getting smarter by the day and adopting new ways to hack systems and gain unauthorised access to confidential information and funds. Failure to monitor and block unusual market activity can be crippling for customers and the institution. This is forcing banks to continuously improve their processes by adopting the highest level of fraud detection systems, and working with data scientists to create algorithms to detect inconsistencies and prevent abnormal user behaviour.

For instance, it is now commonplace that if a financial institution suspects unusual transactions or withdrawals are being made from an account based on historic customer data, users are alerted and further access to the account is blocked until the account owner approves the activity.

Data science and AI is playing a major role in risk management for financial institutions

data science and analytics with AI role in risk management

Data science and artificial intelligence play a significant role in mitigating risk and optimising processes for financial institutions. For example, by creating powerful machine learning algorithms that use historic and real-time customer data to continually monitor market activity, banks are able to recognise the warning signs of potential issues that may occur in future. Another example is the improved efficiency of the overdue payment recovery method, banks now have access to more data and by leveraging this insight through advanced data analytics, they have the ability to reduce the risk of future credit loss by determining the appropriate credit limits and loan amounts to sanction.

Here are just a few of the ways risk analytics is helping financial institutions today:

Risk modelling

Risk modelling enables financial institutions to provide a more sophisticated service to clients and minimise losses. This is achieved by building analytical models using historic data to estimate the degree of risk involved and predicting future financial outcomes to better inform decisions.

Credit policy

The financial services industry is increasingly adopting analytics to develop credit policies and strategies to manage account creation and to maintain and monitor the credit exposure of customers.

Detecting fraud

Using data analytics to prevent fraud before it occurs has become an essential offering for financial institutions wanting to maintain credibility in the market while driving customer conversion and retention.

Proliferation of data science and analytics in the financial investments and trading sector

data science and analytics in financial investments and trading sector

Data-driven investments fuelled by computational mathematics, also popularly known as algorithmic or high-frequency trading, has enabled financial institutions to conduct rapid market analysis resulting in faster and ideally more accurate decisions and timely trading strategies.

Machine learning and AI are also being adopted across world stock markets for a range of reasons, including but not limited to:

  • Data and AI-driven trading systems help to monitor both unstructured and structured data within a fraction of the time when compared to trading systems handled manually, leading to faster decisions and faster transactions.
  • There is a significant increase in accuracy and reliability offered by algorithms that use historical data to predict stock performance.
  • Data analytics provides investors with recommendations based on their long-term and short-term goals.
  • Real-time analytics combined with predictive analytics is helping financial institutions better understand financial trends across the globe.

Artificial intelligence and data science powered personalised banking

Along with digitisation, banks are also focusing on giving customers a unique and relevant experience by deeply personalising and customising their service offerings. Additionally, artificial intelligence and data scientists are helping banks find new and more efficient ways to provide extended benefits and solutions to users. For example, AI chatbots and online finance assistants are allowing customers to get their queries answered 24/7, reducing the overall workload of call centres while also providing a more efficient customer service experience and problem resolution around the clock.

The influx of big data is also providing financial institutions with the opportunity to keep improving and personalising finance management tools. For example, a number of banks are investing in the development of mobile applications that provide personalised financial guidance to enable individuals to more easily meet and manage their financial goals. These applications monitor income, regular expenses, and spending habits to provide customers with tips to optimise their funds, regulate their spending, and tailored financial tips based on their activity and interests as determined by their customer data profile.

Process automation and AI in the financial services industry is the key to decreased operational costs and increased productivity

Robotic process automation is the future of banking and finance. Across the board, AI has encouraged businesses to look at automation as a way to cut down on operational costs and boost overall productivity and effectiveness. Automated systems implemented for high-frequency administrative or repetitive tasks are helping financial institutions to minimise the risk of human error, while also diverting the attention of human labour towards processes that actually require their involvement. For example, AI and automation driven software and processes are allowing banking organisations to more efficiently verify data and maintain data consistency, accelerate compliance, generate reports/merge statements based on set parameters, review documents using natural language processing, collate data from applications and extract insight to identify market trends in real-time, and much more.

However, it is also important to note, in-person customer service and unique conversations with finance experts are still a large part of the customer journey and customer expectations, when it comes to interacting with financial institutions. Data science, data analytics, machine learning and AI is giving the finance industry the power to become more data-driven by optimising and automating a variety of processes, improving products and implementing new solutions, but automation and data insight is also freeing up the time of finance professionals to become more customer focused in real life by simply, understanding their customers better and meeting their needs on an individual level.

With data science and AI shaping the business landscape of the financial services industry and making it easier to perform short-term and long-term tasks, the demand for data professionals with practical data science and analytics skills combined with finance-related domain knowledge has skyrocketed. Those wishing to stay ahead of the game, or arguably in it at all, must be willing to adapt and learn how to use data skills on the job. Finance companies and banks are also pushing their current labour to upskill to data science and AI in order to catch up with emerging technological trends, tools and techniques to stay competitive.


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